Carnosine and Anserine Ingestion Enhances Contribution of Nonbicarbonate Buffering
Bibliographic record
Abstract
INTRODUCTION: Adipose tissue plays complex role(s) in metabolic and endocrine control. To date, little work has been done in the horse regarding adipocytokines. PURPOSE: This study was conducted to determine whether therapeutic levels of chronic beta-agonist administration, exercise, or both could alter their concentrations. METHODS: A total of 23 standard-bred mares were divided into four experimental groups: clenbuterol (2.4 microg.kg(-1) bw twice daily for 8 wk) plus exercise (8 wk, 20 min.d(-1) at 50% VO2max; CLENEX; N = 6), clenbuterol only (CLEN; N = 6), exercise only (EX; N = 5), and control (CON; N = 6). Rump fat thickness was measured using B-mode ultrasound and percent body fat (%fat) was calculated. Plasma adiponectin and leptin concentrations were measured using radioimmunoassay (RIA). In the absence of purified equine adiponectin or leptin, results were expressed as human equivalents of immunoreactive adipocytokines. RESULTS: The change in plasma immunoreactive (ir)-adiponectin HE concentration was negatively correlated (r = -0.520; P = 0.01) to the change in fat mass and positively correlated (r = 0.446; P = 0.03) to the change in fat-free mass. The change in plasma ir-leptin HE concentration was positively correlated (r = 0.550; P = 0.02) to the change in fat mass and negatively correlated (r = -0.473; P < 0.05) to the change in fat-free mass. CONCLUSION: These data demonstrate that a chronic clenbuterol administration alters the concentrations of the adipocytokines adiponectin and leptin in horses. These changes may play a role in previously reported repartitioning effects of clenbuterol.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".